diff --git a/scripts/CMakeLists.txt b/scripts/CMakeLists.txt
index 097cd3f..5bc53bb 100644
--- a/scripts/CMakeLists.txt
+++ b/scripts/CMakeLists.txt
@@ -1,52 +1,56 @@
 add_custom_target(scripts_incl SOURCES
     FindSpecMiCP.cmake
     reactmicp.qsub
     pgo_sequence.sh
     specmicp.pc.in
     reactmicp.pc.in
 
     analyser/reactmicp_unsaturated_analyse.sh
     analyser/analyse_unsaturated_hdf5.py
 )
 
 # the cmake find module
 # ---------------------
 install(FILES FindSpecMiCP.cmake
     DESTINATION ${SHARE_INSTALL_DIR}/cmake
 )
 
 # openPBS submission file
 
 file(COPY reactmicp.qsub DESTINATION ${CMAKE_CURRENT_BINARY_DIR})
 install(FILES reactmicp.qsub
     DESTINATION ${SHARE_INSTALL_DIR}
 )
 
 # Profile guided optimization
 
 file(COPY pgo_sequence.sh DESTINATION ${CMAKE_CURRENT_BINARY_DIR})
 
 # pkg-config files
 configure_file(specmicp.pc.in ${CMAKE_CURRENT_BINARY_DIR}/specmicp.pc @ONLY)
 install(FILES ${CMAKE_CURRENT_BINARY_DIR}/specmicp.pc
     DESTINATION ${LIBRARY_INSTALL_DIR}/pkgconfig
 )
 
 configure_file(reactmicp.pc.in ${CMAKE_CURRENT_BINARY_DIR}/reactmicp.pc @ONLY)
 install(FILES ${CMAKE_CURRENT_BINARY_DIR}/reactmicp.pc
     DESTINATION ${LIBRARY_INSTALL_DIR}/pkgconfig
 )
 
 # analyser
 
 file(COPY
     analyser/reactmicp_unsaturated_analyse.sh
     analyser/analyse_unsaturated_hdf5.py
+    analyser/short_iter.sh
+    analyser/short_iter.py
     DESTINATION ${CMAKE_CURRENT_BINARY_DIR}/analyser
 )
 
 install(FILES
     analyser/reactmicp_unsaturated_analyse.sh
     analyser/analyse_unsaturated_hdf5.py
+    analyser/short_iter.sh
+    analyser/short_iter.py
     DESTINATION ${SHARE_INSTALL_DIR}/analyser
 )
diff --git a/scripts/analyser/short_iter.py b/scripts/analyser/short_iter.py
index 82268a8..0f5583b 100644
--- a/scripts/analyser/short_iter.py
+++ b/scripts/analyser/short_iter.py
@@ -1,75 +1,92 @@
 import pandas
 import numpy as np
 
 def analyse_file(iter_file, out_file):
     df = open_file(iter_file)
     with open(out_file, 'w') as out_file:
         out_file.write("\n")
         analyse_timestep(df, out_file)
         analyse_iter(df, out_file)
+        analyse_return_code(df, out_file)
         analyse_cpu_time(df, out_file)
         analyse_cpu_transport_time(df, out_file)
         analyse_cpu_chemistry_time(df, out_file)
         out_file.write("\n")
 
 def open_file(iter_file):
     df = pandas.read_csv(iter_file,
                 header=1, sep="\t\s*",
                 skiprows=8, skipfooter=14,
                 skip_blank_lines=False, usecols=range(1,11), index_col=False)
     return df
                           
              
 def basic_print(serie, out_file):
     out_file.write("mean : {0:.4f}\n".format(serie.mean()))
     out_file.write("std : {0:.4f}\n".format(serie.std()))
     out_file.write("median : {0:.4f}\n".format(serie.median()))
     out_file.write("min : {0:.4f}\n".format(serie.min()))
     out_file.write("max : {0:.4f}\n".format(serie.max()))
     
     
 def analyse_iter(df, out_file):
     try:
         c_iter = df["Iterations"]
     except KeyError:
         c_iter = df["Iterations "] # ... format ...
                 
     out_file.write("\nIterations\n=========\n")
     basic_print(c_iter, out_file)
     hist_n, hist_range = np.histogram(c_iter)
     out_file.write("\nHistogram : \n")
     for i, n in enumerate(hist_n):
         out_file.write("{0}\t{1}\n".format(hist_range[i], n))
     out_file.write("{0}\t{1}\n".format(hist_range[-1], 0))
 
 def analyse_timestep(df, out_file):
-    t_time = df["dt"]
+    dt_time = df["dt"]
     
-    out_file.write("Nb timesteps : {0}\n".format(t_time.count()))  
+    out_file.write("Nb timesteps : {0}\n".format(dt_time.count()))  
+
+    t_time = df["T"] 
+    ret_code = df["Return_code"]
+    
+    out_file.write("Last timestep : {0} with retcode {1}\n".format(t_time[t_time.last_valid_index()],ret_code[ret_code.last_valid_index()]))
     
     out_file.write("\nTimestep\n=========\n")
-    basic_print(t_time, out_file)
+    basic_print(dt_time, out_file)
+
+def analyse_return_code(df, out_file):
+    ret_code = df["Return_code"]
+
+    out_file.write("\nReturn codes\n=========\n")
+
+    hist_n, hist_range = np.histogram(ret_code, bins=[-3,-2,-1,0,1,2,3,4,5])
+    for i, n in enumerate(hist_n):
+        out_file.write("{0}\t{1}\n".format(hist_range[i], n))
+    out_file.write("{0}\t{1}\n".format(hist_range[-1], 0))
+    
     
 def analyse_cpu_time(df, out_file):
     t_time = df["Time"]
     out_file.write("\nIteration CPU Time\n=========\n")
     basic_print(t_time, out_file)
     
 def analyse_cpu_transport_time(df, out_file):
     t_time = df["Transport_time"]
     out_file.write("\nIteration CPU Transport Time\n=========\n")
     basic_print(t_time, out_file)
         
 def analyse_cpu_chemistry_time(df, out_file):
     t_time = df["Chemistry_time"]
     out_file.write("\nIteration CPU Chemistry Time\n=========\n")
     basic_print(t_time, out_file)
         
     
 if __name__ == "__main__":
     import sys
     analyse_file(sys.argv[1], sys.argv[2])
         
     
                 
-                
\ No newline at end of file
+